- Upload your image by clicking or dragging the file.
- The image is automatically converted to Base64.
- Choose your preferred output format (Data URI, Base64, HTML, or CSS).
- Copy the generated code to use in your project.
What is Base64 encoding?
Base64 is a binary-to-text encoding scheme that represents binary data as ASCII text. It's commonly used to embed images directly in HTML, CSS, or JSON without needing separate image files.
When should I use Base64 images?
Base64 is ideal for small images like icons and logos (under 10KB). For larger images, traditional image files are more efficient as Base64 increases file size by about 33%.
What's the difference between Data URI and raw Base64?
Data URI includes the MIME type prefix (e.g., 'data:image/png;base64,') and can be used directly in HTML src attributes. Raw Base64 is just the encoded string without the prefix.
Base64 Encoding & Decoding Guide【2026】- Free Online Encoder with Code Examples
Master Base64 encoding and decoding with practical examples in JavaScript, Python, and Java. Free online Base64 encoder tool. Learn data URLs, JWT, HTTP authentication, and best practices.
Data URLs Complete Guide【2026】- Inline Asset Embedding Best Practices
Master Data URL principles and applications. Learn image and font inline embedding, optimize web performance. Includes Base64 encoding, caching strategies, performance trade-offs. Complete code examples!
Complete Guide to Image Format Conversion【2026】- PNG, JPG, WebP, SVG Explained
Master image format conversion with our comprehensive guide. Learn when to use PNG, JPG, WebP, and SVG formats, and how to convert between them efficiently.
Base64
Base64 is a binary-to-text encoding scheme that represents binary data in an ASCII string format by translating it into a radix-64 representation. It uses 64 printable characters (A-Z, a-z, 0-9, +, /) to encode binary data.
Text-to-Image
Text-to-Image is an artificial intelligence technology that generates visual images from natural language text descriptions, using deep learning models to interpret textual prompts and synthesize corresponding photorealistic or artistic images.